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Publication Number:  FHWA-HRT-15-019    Date:  May 2015
Publication Number: FHWA-HRT-15-019
Date: May 2015

 

Evaluation of Long-Term Pavement Performance (LTPP) Climatic Data for Use in Mechanistic-Empirical Pavement Design Guide (MEPDG) Calibration and Other Pavement Analysis

Chapter 4. MODERN ERA RETROSPECTIVE-ANALYSIS FOR RESEARCH AND APPLICATIONS (MERRA)

The MERRA product from NASA is a new alternative for obtaining high-quality atmospheric and surface weather history data.(44,45) MERRA is a physics-based reanalysis model that combines computed model fields (e.g., atmospheric temperatures) with ground-, ocean-, atmospheric-, and satellite-based observations that are distributed irregularly in space and time. The result is a uniformly gridded dataset of meteorological data derived from a consistent model and analysis system over the entire data history. MERRA improves on earlier generations of reanalysis models such as those developed by NOAA’s National Center for Environmental Prediction, the European Centre for Medium-Range Weather Forecasts, and the Japan Meteorological Agency.(46,47,48)

Distribution of MERRA data is funded by NASA’s Science Mission Directorate. The data are not copyrighted and are open to all for both commercial and noncommercial uses. NASA uses MERRA to help verify seasonal climate forecasting systems, generate climate data records, serve as input to satellite retrieval algorithms, and provide atmospheric forcings for hydrologic and land surface process studies.(49) In addition, MERRA is regularly evaluated and validated to ensure continuity and consistency because the data product is produced in near real-time.

MERRA data are provided at an hourly temporal resolution and a 0.5-degree by 0.67-degree (latitude/longitude) spatial resolution from 1979 to the present. Figure 7 illustrates graphically the spatial density of MERRA grid points over the continental United States; MERRA spans the entire globe at this spatial resolution. For contrast, figure 8 shows the spatial distribution and density (computed as the number of ASOS stations per MERRA grid pixel) of first-order ASOS ground-based weather stations over the continental United States; the ASOS coverage is limited to the United States. The ASOS OWSs are the primary source of climate data for the MEPDG weather database. The higher spatial resolution and larger geographic scope of the MERRA data are clear. In addition, NASA is currently upgrading MERRA to a 0.62- by 0.62-mi horizontal resolution.

The merger of the GEOS-5 model with observations is based on the Grid-Point Statistical Interpolation (GSI), which is a three-dimensional variational data assimilation analysis algorithm. Prior to assimilation, the available observations undergo a sophisticated QC/quality assurance (QA) procedure. Only observations that pass the QC/QA procedure are used during assimilation. For a given 6-h assimilation window, the GEOS-5 model first predicts the background (predictor) states over which the GSI analysis is computed (figure 9). Next, an incremental analysis update (IAU) procedure is conducted during which the analysis correction is applied to the forecast model gradually over the 6-h assimilation window. In essence, the IAU serves to move the model forecast toward closer agreement with the assimilated observations without introducing abrupt discontinuities or physical inconsistencies into the model dynamics. Once the IAU procedure has completed the given 6-h assimilation window, the model advances forward in time to the next 6-h window. This process is repeated over the course of the more than 30-year observation record. More than 4 million observations (mostly satellite-derived) are typically ingested during a 6-h assimilation cycle. For more details on the GEOS-5 model and the GSI procedure, the reader is referred to Rienecker et al.(44)

Figure 7. Map. Map of MERRA grid points over the continental United States where each grid point is approximately 31.1 by 37.3 mi at mid-latitudes. This figure is a map of the United States. Each State is outlined but not labeled. The map illustrates graphically the spatial density of Modern-Era Retrospective Analysis for Research and Application grid points over the continental United States. The horizontal axis shows the longitude of -125 to -65 and longitude spatial resolution of 0.67 in degrees, and the vertical axis shows the latitude of 25 to 50 and latitude spatial resolution of 0.5 in degrees.

Figure 7. Map. Map of MERRA grid points over the continental United States where each grid point is approximately 31.1 by 37.3 mi at mid-latitudes.

Figure 8. Map. Spatial distribution and density of first-order ASOS stations over the continental United States. This figure is a map of the United States. Each State is outlined but not labeled. The map shows the spatial distribution and density (computed as the number of Automated Surface Observing System (ASOS) stations per Modern-Era Retrospective Analysis for Research and Application (MERRA) grid pixel) of first-order ASOS ground-based weather stations over the continental United States. The density ranges from 0 to 6, with the majority of the map showing a density of 1 or 2 for the ASOS stations. Very few areas have 3 or 4 ASOS stations per MERRA pixel. There are many locations where no ASOS stations are located.

Figure 8. Map. Spatial distribution and density of first-order ASOS stations over the continental United States.

Figure 9. Illustration. Schematic of the IAU procedure in MERRA (from Rienecker et al.) shown in Greenwich Mean Time. This figure depicts the IAU procedure. The numbers along the top represent Greenwich Mean Time. Along the top of the illustration, there are times spaced equidistant starting at 03Z by increments of 03 until it reaches 03Z again (03Z, 06Z, 09Z, 12Z, 15Z, 18Z, 21Z, 00Z, and 03Z). The first 6 h from 03Z to 09Z are depicted as a corrector segment. At times 09Z, 12Z, and 15Z there are circles that represent the background predictor states. These states are joined together by lines to form a triangle that meets with a diamond that represents Grid-Point Statistical Interpolation Analysis. From this point, the analysis increment is applied to meet with the Initial States for Corrector under 09Z. From 09Z, the corrector segment extends 6 h and shows Analysis tendencies above along the way. This 6-h process is repeated continuously.

Source: M.M. Rienecker et al.

Figure 9. Illustration. Schematic of the IAU procedure in MERRA (from Rienecker et al.) shown in Greenwich Mean Time.(44)

MERRA is capable of providing all of the weather history inputs required by the MEPDG and other current infrastructure applications. Table 2 contains the MERRA data elements used to develop MEPDG weather history inputs. In addition, MERRA contains additional data elements useful for enhancements of current infrastructure applications and/or for support of future applications. Samples of available data elements are provided in table 3. A complete listing of all MERRA data elements can be found at http://gmao.gsfc.nasa.gov/products/documents/MERRA_File_Specification.pdf.

Table 2. MERRA data elements available to develop MEPDG weather history inputs.(45)

Element Description Units
CF Total cloud fraction fraction
PPT Precipitation flux incident upon the ground surface kg H2O m2 s-1
PS Surface pressure at 2 m above ground surface Pa
Q Specific humidity at 2 m above ground surface kg H2O kg-1 air
Rsw Shortwave radiation incident upon the ground surface W m-2
Rtoa Shortwave radiation incident at the top of atmosphere W m-2
T Air temperature at 2 m above ground surface K
U Eastward wind at 2 m above ground surface m s-1
V Northward wind at 2 m above ground surface m s-1

 

Table 3. Examples of other MERRA data elements of potential interest for transportation infrastructure applications.(45)

Element Description Units
T Air temperature at 10-meters above ground surface1 K
U Eastward wind at 10-meters above ground surface1 m s-1
V Northward wind at 10-meters above ground surface1 m s-1
PRMC Total profile soil moisture content m3 m-3
RZMC Root zone soil moisture content m3 m-3
SFMC Top soil layer soil moisture content m3 m-3
TSURF Mean land surface temperature (including snow) K
TSOIL Soil temperature in layer (available for 6 soil layers) K
PRECSNO Surface snowfall kg m-2 s-1
SNOMAS Snow mass kg m-2
SNODP Snow depth m
EVPSOIL Bare soil evaporation W m-2
EVPTRNS Transpiration W m-2
EVPSBLN Sublimation W m-2
QINFIL Soil water infiltration rate kg m-2 s-1
SHLAND Sensible heat flux from land W m-2
LHLAND Latent heat flux from land W m-2
EVLAND Evaporation from land kg m-2 s-1
LWLAND Net downward longwave flux over land W m-2
SWLAND Net downward shortwave flux over land W m-2
EMIS Surface emissivity fraction
ALBEDO Surface albedo fraction
1Also available at approximately 100 m and other elevations.

MERRA is freely available to all research agencies and universities through the NASA Modeling and Assimilation Data and Information Services Center (MDISC) at http://disc.sci.gsfc.nasa.gov/daac-bin/DataHoldings.pl. Subsets of the MERRA data at an hourly resolution as a function of time and space can be requested from MDISC, including specification of the desired data element(s). Once the timeframe, region of the globe, and MERRA data elements of interest are selected, the data files are retrieved by NASA in Hierarchical Data Format (HDF) or Network Common Data Form (NetCDF) format. The HDF and NetCDF supercomputer data formats are the only data formats currently supported by NASA to keep storage of the large dataset sizes tractable. Once NASA retrieves the requested data files, they are posted to a public FTP server for access by the user. A number of computing languages (e.g., C++, FORTRAN, IDL, and MATLAB®) contain HDF and NetCDF libraries that can be employed to open the MERRA data files, extract the relevant locations in space and time, and prepare the data for subsequent use in MEPDG or any other infrastructure application of interest.

 

 

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